High-density scalp electroencephalogram dataset during sensorimotor rhythm-based brain-computer interfacing

Abstract

Real-time functional imaging of human neural activity and its closed-loop feedback enable voluntary control of targeted brain regions. In particular, a brain-computer interface (BCI), a direct bridge of neural activities and machine actuation is one promising clinical application of neurofeedback. Although a variety of studies reported successful self-regulation of motor cortical activities probed by scalp electroencephalogram (EEG), it remains unclear how neurophysiological, experimental conditions or BCI designs influence variability in BCI learning. Here, we provide the EEG data during using BCIs based on sensorimotor rhythm (SMR), consisting of 4 separate datasets. All EEG data were acquired with a high-density scalp EEG setup containing 128 channels covering the whole head. All participants were instructed to perform motor imagery of right-hand movement as the strategy to control BCIs based on the task-related power attenuation of SMR magnitude, that is event-related desynchronization. This dataset would allow researchers to explore the potential source of variability in BCI learning efficiency and facilitate follow-up studies to test the explicit hypotheses explored by the dataset.

Fig. 1

Experimental configuration. (a) Electrode locations used for online BCI operation. The pink cross indicates the electrode around the sensorimotor cortex contralateral to the right hand (C3 channel in the international 10–20 system) and blue crosses indicate the reference electrode used for the large-Laplacian filter. (b) Time-courses of a trials used in each dataset.

Fig. 2

Power spectra derived from four datasets. Gray lines indicate the average value from single participant and black line indicates the global mean. Four panels represent scalp EEG signals derived from C3 channel from each dataset, respectively.

Fig. 3

Time-frequency representations and Topographic maps of SMR-ERD from each dataset. (a) Time-frequency maps of scalp EEG signals around the sensorimotor cortex contralateral to the right hand (C3 channel). Across datasets, the task-related power attenuation in 8–30 Hz was observed during imagery period. (b) Topographic representations of the task-related spectral power change in the alpha-band (8–13 Hz). The white cross indicates C3 channel. (c) Topographic representations of the task-related spectral power change in the beta-band (14–30 Hz).

Fig. 4

Neural decoding performance using spectral power-based featuresNeural decoding performance to classify the spectral EEG power into the resting-state and motor imagery task was analyzed in a participant-by-participant manner. The distribution of cross-validated accuracy was visualized using the violin plot. The red dashed line indicates the chance-level accuracy.